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monte carlo mark Discussed on Gravel Notes with David Evans
Gravel Notes with David Evans
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Aired 2 years ago 33:59
LM101-066: How to Solve Constraint Satisfaction Problems using MCMC Methods (Rerun)
In this episode of Learning Machines 101 (www.learningmachines101.com) we discuss how to solve constraint satisfaction inference problems where knowledge is represented as a large unordered collection of complicated probabilistic constraints among a collection of variables. The goal of the inference process is to infer the most probable values of the unobservable variables given the observable variables. Specifically, Monte Carlo Markov Chain ( MCMC ) methods are discussed.
Aired 2 years ago 25:39
LM101-067: How to use Expectation Maximization to Learn Constraint Satisfaction Solutions (Rerun)
In this episode we discuss how toÂ learnÂ to solve constraint satisfaction inference problems. The goal of the inference process is to infer the most probable values for unobservable variables. These constraints, however, can beÂ learnedÂ from experience. Specifically, the important machine learning method for handling unobservable components of the data using Expectation Maximization is introduced. Check it out at: www.learningmachines101.com Â
Aired 1 year ago 69:12
#166: Jai Alai
Call our Voicemail Hotline: 951-292-4377;Â Gambling in Monte Carlo;Â Karma at the craps table;Â Tracking play;Â Jai alai;Â Commission-free pai gow poker at Ocean's Eleven;Â Wendover trip report;Â Soaring Eagle trip report;Â Tuna for tips;Â Sports-betting "statistics"